System and method for processing directly-collected normalized voice feedback for use in automated patient care

ABSTRACT

A system and method for transacting an automated patient communications session is described. A patent health condition is monitored by regularly collecting physiological measures through an implantable medical device. A patient communications session is activated through a patient communications interface, including an implantable microphone and an implantable speaker in response to a patient-provided activation code. An identification of the patient is authenticated based on pre-defined uniquely identifying patient characteristics. Spoken patient information is received through the implantable microphone and verbal system information is played through the implantable speaker. The patient communications session is terminated by closing the patient communications interface. The physiological measures and the spoken patient information are sent.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a divisional of U.S. patent application Ser.No. 11/089,839, filed Mar. 25, 2005, pending, which is acontinuation-in-part of U.S. Pat. No. 6,997,873, issued Feb. 14, 2006,which is a continuation of U.S. Pat. No. 6,261,230, issued Jul. 17,2001, which is a continuation-in-part of U.S. Pat. No. 6,203,495, issuedMar. 20, 2001, which is a continuation-in-part of U.S. Pat. No.6,312,378, issued Nov. 6, 2001, the priority dates of which ate claimedand the disclosures of which are incorporated by reference.

FIELD

The present invention relates in general to automated data collectionand analysis, and, in particular, to a system and method for transactingan automated patient communications session.

BACKGROUND

A broad class of medical subspecialties, including cardiology,endocrinology, hematology, neurology, gastroenterology, urology,ophthalmology, and otolaryngology, to name a few, rely on accurate andtimely patient information for use in aiding health care providers indiagnosing and treating diseases and disorders. Often, proper medicaldiagnosis requires information on physiological events of short durationand sudden onset, yet these types of events are often occur infrequentlyand with little or no warning. Fortunately, such patient information canhe obtained via external, implantable, cutaneous, subcutaneous, andmanual medical devices, and combinations thereof. For example, in thearea of cardiology, implantable poise generators (IPGs) are medicaldevices commonly used to treat irregular heartbeats, known asarrhythmias. There are three basic types of IPGs. Cardiac pacemakers areused to manage bradycardia, an abnormally slow or irregular heartbeat.Bradycardia can cause symptoms such as fatigue, dizziness, and fainting.Implantable cardioverter defibrillators (ICDs) are used to treattachycardia, heart rhythms that are abnormally fast and lifethreatening. Tachycardia can result in sudden cardiac death (SCD).Finally, implantable cardiovascular monitors and therapeutic devices areused to monitor and treat structural problems of the heart, such, ascongestive heart failure, as well as rhythm problems.

Pacemakers and ICDs, as well as other types of implantable and externalmedical devices, are equipped with an on-board, volatile memory in whichtelemetered signals can be stored for later retrieval and analysis. Inaddition, a growing class of cardiac medical devices, includingimplantable heart failure monitors, implantable event monitors,cardiovascular monitors, and therapy devices, are being used to providesimilar stored device information. These devices are able to store morethan thirty minutes of per heartbeat data. Typically, the telemeteredsignals can provide patient device information recorded on a perheartbeat, binned average basis, or derived basis from, for example,atrial electrical activity, ventricular electrical activity, minuteventilation, activity score, cardiac output score, mixed venous oxygenscore, cardiovascular pressure measures, time of day, and interventionsand the relative success of interventions. In addition, many suchdevices can have multiple sensors, or several devices can work together,for monitoring different sites within a patient's body.

Presently, stored device information is retrieved using a proprietaryinterrogator or programmer, often during a clinic visit or following adevice event. The volume of data retrieved from a single deviceinterrogation “snapshot” can be large and proper interpretation andanalysis can require significant physician time and detailedsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists. The sequential logging and analysis of regularlyscheduled interrogations can create an opportunity for recognizingsubtle and incremental changes in patient condition otherwiseundetectable by inspection of a single “snapshot.” However, presentapproaches to data interpretation and understanding and practicallimitations on rime and physician availability make such analysisimpracticable.

Similarly, the determination and analysis of the quality of life issueswhich typically accompany the onset of a chronic yet stable diseases,such as coronary-artery disease, is a crucial adjunct to assessingpatient wellness and progress. However, unlike in a traditional clinicalsetting, physicians participating in providing remote patient care arenot able to interact with their patients in person. Consequently,quality of life measures, such as how the patient subjectively looks andfeels, whether the patient has shortness of breath, can work, can sleep,is depressed, is sexually active, can perform activities of daily life,and so on, cannot be implicitly gathered and evaluated.

A prior art system for collecting and analyzing pacemaker and ICDtelemetered signals in a clinical or office setting is the Model 9790Programmer, manufactured by Medtronic, Inc., Minneapolis, Minn. Thisprogrammer can be used to retrieve data, such as electrocardiogram andany measured physiological conditions, collected by the IPG forrecordation, display and printing. The retrieved data is displayed inchronological order and analyzed by a physician. Comparable prior artsystems are available from other IPG manufacturers, such as the Model2901 Programmer Recorder Monitor, manufactured by Guidant Corporation,Indianapolis, Ind., which includes a removable floppy diskette mechanismfor patient data storage. These prior art systems lack remotecommunications facilities and must be operated with the patient present.These systems present a limited analysis of the collected data based ona single device interrogation and lack the capability to recognizetrends in the data spanning multiple episodes over time or relative to adisease specific peer group.

A prior art system for locating and communicating with a remote medicaldevice implanted in an ambulatory patient is disclosed in U.S. Pat. No.5,752,976 ('976). The implanted device includes a telemetry transceiverfor communicating data and operating instructions between the implanteddevice and an external patient communications device. The communicationsdevice includes a link to a remote medical support network, a globalpositioning satellite receiver, and a patient activated link forpermitting patient initiated communication with the medical supportnetwork. Patient voice communications through the patient link includeboth actual patient voice and manually actuated signaling which mayconvey an emergency situation. The patient voice is converted to anaudio signal, digitized, encoded, and transmitted by data bus to asystem controller.

Related prior art systems for remotely communicating with and receivingtelemetered signals from a medical device are disclosed in U.S. Pat.Nos. 5,113,869 ('869) and 5,336,245 ('245). In the '869 patent, animplanted AECG monitor can be automatically interrogated at preset timesof day to telemeter out accumulated data to a telephonic communicator ora full disclosure recorder. The communicator can be automaticallytriggered to establish a telephonic link and transmit the accumulateddata to an office or clinic through a modem. In the '245 patent,telemetered data is downloaded to a larger capacity, external datarecorder and is forwarded to a clinic using an auto-dialer and fax modemoperating in a personal computer-based programmer/interrogator. However,the '976 telemetry transceiver, '869 communicator, and '245programmer/interrogator are limited to facilitating communication andtransferal of downloaded patient data and do not include an ability toautomatically track, recognize, and analyze trends in the data itself.Moreover, the '976 telemetry transceiver facilitates voicecommunications through transmission of a digitized audio signal and doesnot perform voice recognition or other processing to the patient'svoice.

The uses of multiple sensors situated within a patient's body atmultiple sites are disclosed in U.S. Pat. No. 5,040,536 ('536) and U.S.Pat. No. 5,987,352 ('352). In the '536 patent, an intravascular pressureposture detector includes at least two pressure sensors implantedindifferent places in the cardiovascular system, such that differencesin pressure with changes in posture are differentially measurable.However, the physiological measurements are used locally within thedevice, or in conjunction with any implantable device, to effect atherapeutic treatment. In the '352 patent, an event monitor can includeadditional sensors for monitoring and recording physiological signalsduring arrhythmia and syncopal events. The recorded signals can be usedfor diagnosis, research or therapeutic study, although no systematicapproach to analyzing these signals, particularly with respect to peerand general population groups, is presented.

Thus, there is a need for a system and method for providing continuousretrieval, transferal, and automated analysis of medical deviceinformation, such as telemetered signals, retrieved in general fromabroad class of implantable and external medical devices. Preferably,the automated analysis would include recognizing a trend indicatingdisease absence, onset, progression, regression, and status quo anddetermining whether medical intervention is necessary.

There is a further need for a system and method that would allowconsideration of sets of collected measures, both actual and derived,from multiple device interrogations. These collected measures sets couldthen be compared and analyzed against short and long term periods ofobservation.

There is a further need for a system and method that would enable themeasures sets for an individual patient to be self-referenced andcross-referenced to similar or dissimilar patients and to the generalpatient population. Preferably, the historical collected measures setsof a patient could be compared and analyzed against those of otherpatients in general or of a disease specific peer group.

There is a further need for a system and method for accepting andnormalizing live voice feedback spoken by an individual patient while anidentifiable set of telemetered signals is collected by a implantablemedical device. Preferably, the normalized voice feedback asemi-quantitative self-assessment of an individual patient's physicaland emotional well being at a time substantially contemporaneous to thecollection of the telemetered signals.

SUMMARY

The present invention provides a system and method for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care. The patient deviceinformation relates to individual measures recorded by and retrievedfrom implantable medical devices, such as IPGs and monitors. The patientdevice information is received on a regular, e.g., daily, basis as setsof collected measures which are stored with other patient records in adatabase. The information can be analyzed in an automated fashion andfeedback provided to the patient at any time and in any location.

The present invention also provides a system and method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system. As before, patient deviceinformation is received on a regular, e.g., daily, basis as sets ofcollected measures which are stored along with other patient records ina database. Voice feedback spoken by an individual patient is processedinto a set of quality of life measures by a remote client substantiallycontemporaneous to the recordation of an identifiable set of collecteddevice measures by the implantable medical device. The processed voicefeedback and identifiable collected device measures set are bothreceived and stored into the patient record in the database forsubsequent evaluation.

An embodiment is a system and method for transacting an automatedpatient communications session. A patent health condition is monitoredby regularly collecting physiological measures through an implantablemedical device. A patient communications session is activated through apatient communications interface, including an implantable microphoneand an implantable speaker in response to a patient-provided activationcode. An identification of the patient is authenticated based onpre-defined uniquely identifying patient characteristics. Spoken patientinformation is received through the implantable microphone and verbalsystem information is played through the implantable speaker. Thepatient communications session is terminated by closing the patientcommunications interface. The physiological measures and the spokenpatient information are sent.

A further embodiment is a system and method for processingdirectly-collected normalized voice feedback for use in automatedpatient care. One or more physiological measures relating to individualpatient information and one or more quality of life measures relating tonormalized spoken patient self-assessment indicators are retrieved froma patient care record. Each quality of life measure is recordedsubstantially contemporaneous to the physiological measures through animp tamable microphone. The physiological measures and thecontemporaneously recorded quality of life measures retrieved from onesuch patient care record are analyzed to determine a patient status.

A further embodiment, is a system and method for solicitingdirectly-collected normalized voice feedback for use in automatedpatient care. One or more physiological measures relating to individualpatient information are obtained from a medical device having a sensormonitoring and recording from an anatomical site at least one ofdirectly and derivatively. One or more quality of life measures relatingto normalized spoken patient self-assessment indicators are recordedthrough an implantable microphone. Each quality of life measure isrecorded substantially contemporaneous to the physiological measures.The physiological measures and the quality of life measures are storedin patient care records.

The present invention facilitates the gathering, storage, and analysisof critical patient information obtained on a routine basis and analyzedin an automated manner. Thus, the burden on physicians and trainedpersonnel to evaluate the volumes of information is significantlyminimized while the benefits to patients are greatly enhanced.

The present invention also enables the simultaneous collection of bothphysiological measures from implantable medical devices and quality oflife measures spoken in the patient's own words. Voice recognitiontechnology enables the spoken patient feedback to be normalized to astandardized set of semi-quantitative quality of life measures, therebyfacilitating holistic remote, automated patient care.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 2 is a block diagram showing the hardware components of the serversystem of the system of FIG. 1;

FIG. 3 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 4 is a block diagram showing the analysis module of the serversystem of FIG. 3;

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored in the database of the system ofFIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database of the system ofFIG. 1;

FIG. 7 is a flow diagram showing a method for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 8 is a flow diagram showing a routine for analyzing collectedmeasures sets for use in the method of FIG. 7;

FIG. 9 is a flow diagram showing a routine for comparing siblingcollected measures sets for use in the routine of FIG. 8;

FIGS. 10A and 10B are flow diagrams showing a routine for comparing peercollected measures sets for use in the routine of FIG. 8;

FIG. 11 is a flow diagram showing a routine for providing feedback foruse in the method of FIG. 7;

FIG. 12 is a block diagram showing a system for providing normalizedvoice feedback from an individual patient in an automated collection andanalysis patient care system;

FIG. 13 is a block diagram showing the software modules of the remoteclient of the system of FIG. 12;

FIG. 14 is a block diagram showing the software modules of the serversystem of the system of FIG. 12;

FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record for cardiac patient care storedas part of a patient care record in the database of the system of FIG.12;

FIGS. 16A-16B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system;

FIG. 17 is a flow diagram showing a routine for processing voicefeedback for use in the method of FIGS. 16A-16B;

FIG. 18 is a flow diagram showing a routine for requesting a quality oflife measure for use in the routine of FIG. 17;

FIG. 19 is a flow diagram showing a routine for recognizing andtranslating individual spoken words for use in the routine of FIG. 17;

FIG. 20 is a block diagram showing the software modules of the serversystem in a further embodiment of the system of FIG. 12;

FIG. 21 is a block diagram showing a system for providing normalizedvoice feedback from an individual patient in an automated collection andanalysis patient care system in accordance with a further embodiment ofthe present invention;

FIG. 22 is a block diagram showing the analysis module of the serversystem of FIG. 21;

FIG. 23 is a database schema showing, by way of example, dieorganization of a quality of life and symptom measures set record forcare of patients stored as part of a patient care record in the databaseof the system of FIG. 21;

FIG. 24 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database of the system ofFIG. 21;

FIG. 25 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records of FIG. 24;

FIGS. 26A-26B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system in accordance with a furtherembodiment of the present invention; and

FIG. 27 is a block diagram showing a system for providing directnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system in accordance with a furtherembodiment of the present invention.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing a system 10 for automated collectionand analysis of patient information retrieved from an implantablemedical device for remote patient care in accordance with the presentinvention. A patient 11 is a recipient of an implantable medical device12, such as, by way of example, an IPG or a heart failure or eventmonitor, with a set of leads extending into his or her heart. Theimplantable medical device 12 includes circuitry for recording into ashort-term, volatile memory telemetered signals, which are stored as aset of collected measures for later retrieval.

For an exemplary cardiac implantable medical device, the telemeteredsignals non-exclusively present patient information recorded on a perheartbeat, binned average or derived basis and relating to: atrialelectrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygenation score, cardiovascular pressure measures, time of day, thenumber and types of interventions made, and the relative success of anyinterventions, plus the status of the batteries and programmed settings.Examples of pacemakers suitable for use in the present invention includethe Discovery line of pacemakers, manufactured by Guidant Corporation,Indianapolis, Ind. Examples of ICDs suitable for use in the presentinvention include the Gem line of ICDs, manufactured by MedtronicCorporation, Minneapolis, Minn.

In the described embodiment, the patient 11 has a cardiac implantablemedical device. However, a wide range of related implantable medicaldevices are used in other areas of medicine and a growing number ofthese devices are also capable of measuring and recording patientinformation for later retrieval. These implantable medical devicesinclude monitoring and therapeutic devices for use in metabolism,endocrinology, hematology, neurology, muscular disorders,gastroenterology, urology, ophthalmology, otolaryngology, orthopedics,and similar medical subspecialties. One skilled in the art would readilyrecognize the applicability of the present invention to these relatedimplantable medical devices.

On a regular basis, the telemetered signals stored in the implantablemedical device 12 are retrieved. By way of example, a programmer 14 canbe used to retrieve the telemetered signals. However, any form ofprogrammer, interrogator, recorder, monitor, or telemetered signalstransceiver suitable for communicating with an implantable medicaldevice 12 could be used, as is known in the art. In addition, a personalcomputer or digital data, processor could be interfaced to theimplantable medical device 12, either directly or via a telemeteredsignals transceiver configured to communicate with the implantablemedical device 12.

Using the programmer 14, a magnetized reed switch (not shown) within theimplantable medical device 12 closes in response to the placement of awand 13 over the location of the implantable medical device 12. Theprogrammer 14 communicates with the implantable medical device 12 via RFsignals exchanged through the wand 13. Programming or interrogatinginstructions are sent to the implantable medical device 12 and thestored telemetered signals are downloaded into the programmer 14. Oncedownloaded, the telemetered signals are sent via an internetwork 15,such as the Internet, to a server system 16 which periodically receivesand stores the telemetered signals in a database 17, as furtherdescribed below with reference to FIG. 2.

An example of a programmer 14 suitable for use in the present inventionis the Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes the capability to storeretrieved telemetered signals on a proprietary removable floppydiskette. The telemetered signals could later be electronicallytransferred using a personal computer or similar processing device tothe internetwork 15, as is known in the art.

Other alternate telemetered signals transfer means could also beemployed. For instance, the stored telemetered signals could beretrieved from the implantable medical device 12 and electronicallytransferred to the internetwork 15 using the combination of a remoteexternal programmer and analyzer and a remote telephonic communicator,such as described in U.S. Pat. No. 5,113,869, the disclosure of which isincorporated herein by reference. Similarly, the stored telemeteredsignals could be retrieved and remotely downloaded to the server system16 using a world-wide patient location and data telemetry system, suchas described in U.S. Pat. No. 5,752,976, the disclosure of which isincorporated herein by reference.

The received telemetered signals are analyzed by the server system 16,which generates a patient status indicator. The feedback is thenprovided back to the patient 11 through a variety of means. By way ofexample, the feedback can be sent as an electronic mail messagegenerated automatically by the server system 16 for transmission overthe internetwork 15. The electronic mail message is received by a remoteclient 18, such as a personal computer (PC), situated for local accessby the patient 11. Alternatively, the feedback can be sent through atelephone interface device 19 as an automated voice mail message to atelephone 21 or as an automated facsimile message to a facsimile machine22, both also situated for local access by the patient 11. In additionto a remote client 18, telephone 21, and facsimile machine 22, feedbackcould be sent to other related devices, including a network computer,wireless computer, personal data assistant, television, or digital dataprocessor. Preferably, the feedback is provided in a tiered fashion, asfarther described below with reference to FIG. 3.

FIG. 2 is a block diagram showing the hardware components of the serversystem 16 of the system 10 of FIG. 1. The server system 16 consists ofthree individual servers: network server 31, database server 34, andapplication server 35. These servers are interconnected via anintranetwork 33. In the described embodiment, the functionality of theserver system 16 is distributed among these three servers for efficiencyand processing speed, although the functionality could also be performedby a single server or cluster of servers. The network server 31 is theprimary interface of the server system 16 onto the internetwork 15. Thenetwork server 31 periodically receives the collected telemeteredsignals sent by remote implantable medical devices over the internetwork15. The network server 31 is interfaced to the internetwork 15 through arouter 32. To ensure reliable data exchange, the network server 31implements a TCP/IP protocol stack, although other forms of networkprotocol stacks are suitable.

The database server 34 organizes the patient care records in thedatabase 17 and provides storage of and access to information held inthose records. A high volume of data in the form of collected measuressets from individual patients is received. The database server 34 freesthe network server 31 from having to categorize and store the individualcollected measures sets in the appropriate patient care record.

The application server 35 operates management applications and performsdata analysis of the patient care records, as further described belowwith reference to FIG. 3. The application server 35 communicatesfeedback to the individual patients either through electronic mail sentback over the internetwork 15 via the network server 31 or as automatedvoice mail or facsimile messages through the telephone interface device19.

The server system 16 also includes a plurality of individualworkstations 36 (WS) interconnected to the intranetwork 33, some ofwhich can include peripheral devices, such as a printer 37. Theworkstations 36 are for use by the data management and programmingstaff, nursing staff, office staff, and other consultants and authorizedpersonnel.

The database 17 consists of a high-capacity storage medium configured tostore individual patient care records and related health careinformation. Preferably, the database 17 is configured as a set ofhigh-speed, high capacity hard drives, such as organized into aRedundant Array of Inexpensive Disks (RAID) volume. However, any form ofvolatile storage, non-volatile storage, removable storage, fixedstorage, random access storage, sequential access storage, permanentstorage, erasable storage, and the like would be equally suitable. Theorganization of the database 17 is further described below withreference to FIG. 3.

The individual servers and workstations are general purpose, programmeddigital computing devices consisting of a central processing unit (CPU),random access memory (RAM), non-volatile secondary storage, such as ahard drive or CD ROM drive, network interfaces, and peripheral devices,including user interfacing means, such as a keyboard and display.Program code, including software programs, and data are loaded into theRAM for execution and processing by the CPU and results are generatedfor display, output, transmittal, or storage. In the describedembodiment, the individual servers are Intel Pentium-based serversystems, such as available from Dell Computers, Austin, Tex., or CompaqComputers, Houston, Tex. Each system is preferably equipped with 128 MBRAM, 100 GB hard drive capacity, data backup facilities, and relatedhardware for interconnection to the internetwork 33 and internetwork 15.In addition, the workstations 36 are also Intel Pentium-based personalcomputer or workstation systems, also available from Dell Computers,Austin, Tex., or Compaq Computers, Houston, Tex. Each workstation ispreferably equipped with 64 MB RAM, 10 GB hard drive capacity, andrelated hardware for interconnection to the intranetwork 33. Other typesof server and workstation systems, including personal computers,minicomputers, mainframe computers, supercomputers, parallel computers,workstations, digital data processors and the like would be equallysuitable, as is known in the art.

The telemetered signals are communicated over an internetwork 15, suchas the Internet. However, any type of electronic communications linkcould be used, including an internetwork, link, serial link, datatelephone link, satellite link, radio-frequency link, infrared link,fiber optic link, coaxial cable link, television link, and the like, asis known in the art. Also, the network server 31 is interfaced to theinternetwork 15 using a T-1 network rooter 32, such as manufactured byCisco Systems, Inc., San Jose, Calif. However, any type of interfacingdevice suitable for interconnecting a server to a network could be used,including a data modem, cable modem, network interface, serialconnection, data port, hub, frame relay, digital PBX, and the like, asis known in the art.

FIG. 3 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU as object or byte code, as is known in the arts. The variousimplementations of the source code and object and byte codes can be heldon a computer-readable storage medium or embodied on a transmissionmedium in a carrier wave. There are three basic software modules, whichfunctionally define the primary operations performed by the serversystem 16: database module 51, analysis module 53, and feedback module55. In the described embodiment, these modules are executed in adistributed computing environment, although a single server or a clusterof servers could also perform the functionality of the modules. Themodule functions are further described below in more detail beginningwith reference to FIG. 7.

For each patient being provided remote patient care, the server system16 periodically receives a collected measures set 50 which is forwardedto the database module 51 for processing. The database module 51organizes the individual patent care records stored in the database 52and provides the facilities for efficiently storing and accessing thecollected measures sets 50 and patient data maintained in those records.An exemplary database schema for use in storing collected measures sets50 in a patient care record is described below, by way of example, withreference to FIG. 5. The database server 34 (shown in FIG. 2) performsthe functionality of the database module 51. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

The analysis module 53 analyzes die collected measures sets 50 stored inthe patient care records in the database 52. The analysis module 53makes an automated determination of patient wellness in the form of apatient status indicator 54. Collected measures sets 50 are periodicallyreceived from implantable medical devices and maintained by the databasemodule 51 in the database 52. Through the use of this collectedinformation, the analysis module 53 can continuously follow the medicalwell being of a patient and can recognize any trends in the collectedinformation that might warrant medical intervention. The analysis module53 compares individual measures and derived measures obtained from boththe care records for the individual patient and the care records for adisease specific group of patients or the patient population in general.The analytic operations performed by the analysis module 53 are furtherdescribed below with reference to FIG. 4. The application server 35(shown in FIG. 2) performs the functionality of the analysis module 53.

The feedback module 55 provides automated feedback to the individualpatient based, in part, on the patient status indicator 54. As describedabove, the feedback could be by electronic mail or by automated voicemail or facsimile. Preferably, the feedback is provided in a tieredmanner. In the described embodiment, four levels of automated feedbackare provided. At a first level, an interpretation of the patient statusindicator 54 is provided. At a second level, a notification of potentialmedical concern based on the patient status indicator 54 is provided.This feedback level could also be coupled with human contact byspecially trained technicians or medical personnel. At a third level,the notification of potential medical concern is forwarded to medicalpractitioners located in the patient's geographic area. Finally, at afourth level, a set of reprogramming instructions based on the patientstatus indicator 54 could be transmitted directly to the implantablemedical device to modify the programming instructions contained therein.As is customary in the medical arts, the basic tiered feedback schemewould be modified in the event of bona fide medical emergency. Theapplication server 35 (shown in FIG. 2) performs the functionality ofthe feedback module 55.

FIG. 4 is a block diagram showing the analysis module 53 of the serversystem 16 of FIG. 3. The analysis module 53 contains two functionalsubmodules: comparison module 62 and derivation module 63. The purposeof the comparison module 62 is to compare two or more individualmeasures, either collected or derived. The purpose of the derivationmodule 63 is to determine a derived measure based on one or morecollected measures which is then used by the comparison module 62. Forinstance, a new and improved indicator of impending heart failure couldbe derived based on the exemplary cardiac collected measures setdescribed with reference to FIG. 5. The analysis module 53 can operateeither in a batch mode of operation wherein patient status indicatorsare generated for a set of individual patients or in a dynamic modewherein a patient status indicator is generated on the fly for anindividual patient.

The comparison module 62 receives as inputs from the database 17 twoinput sets functionally defined as peer collected measures sets 60 andsibling collected measures sets 61, although in practice, the collectedmeasures sets are stored on a per sampling basis. Peer collectedmeasures sets 60 contain individual collected measures sets that allrelate to the same type of patient information, for instance, atrialelectrical activity, but which have been periodically collected overtime. Sibling collected measures sets 61 contain individual collectedmeasures sets that relate to different types of patient information, butwhich may have been collected at the same time or different times. Inpractice, the collected measures sets are not separately stored as“peer” and “sibling” measures. Rather, each individual patient carerecord stores multiple sets of sibling collected measures. Thedistinction between peer collected measures sets 60 and siblingcollected measures sets 61 is further described below with reference toFIG. 6.

The derivation module 63 determines derived measures sets 64 on anas-needed basis in response to requests from the comparison module 62.The derived measures 64 are determined by performing linear andnon-linear mathematical operations on selected peer measures 60 andsibling measures 61, as is known in the art.

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored 70 in the database 17 of thesystem 10 of FIG. 1. Only the information pertaining to collectedmeasures sets are shown. Each patient care record would also containnormal identifying and treatment profile information, as well as medicalhistory and other pertinent data (not shown). Each patient care recordstores a multitude of collected measures sets for an individual patient.Each individual set represents a recorded snapshot of telemeteredsignals data which was recorded, for instance, per heartbeat or binnedaverage basis by the implantable medical device 12. For example, for acardiac patient, the following information would be recorded as acollected measures set; atrial electrical activity 71, ventricularelectrical activity 72, time of day 73, activity level 74, cardiacoutput 75, oxygen level 76, cardiovascular pressure measures 77,pulmonary measures 78, interventions made by the implantable medicaldevice 78, and the relative success of any interventions made 80. Inaddition, the implantable medical device 12 would also communicatedevice specific information, including battery status 81 and programsettings 82. Other types of collected measures are possible. Inaddition, a well-documented set of derived measures can be determinedbased on the collected measures, as is known in the art.

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database 17 of the system 10of FIG. 1. Three patient care records are shown for Patient 1, Patient2, and Patient 3. For each patent, three sets of measures are shown, X,Y, and Z. The measures are organized into sets with Set 0 representingsibling measures made at a reference time t=0. Similarly, Set n−2, Setn−1 and Set n each represent sibling measures made at later referencetimes t=n−2, t=n−1 and t=n, respectively.

For a given patient, for instance. Patient 1, all measures representingthe same type of patient information, such as measure X, are peermeasures. These are measures, which are monitored over time in adisease-matched peer group. All measures representing different types ofpatient information, such as measures X, Y, and Z, are sibling measures.These are measures which are also measured over time, but which mighthave medically significant meaning when compared to each other within asingle set. Each of the measures, X, Y, and Z, could be either collectedor derived measures.

The analysis module 53 (shown in FIG. 4) performs two basic forms ofcomparison. First, individual measures for a given patient can becompared to other individual measures for that same patient. Thesecomparisons might be peer-to-peer measures projected over time, forinstance, X_(n), X_(n−1), X_(n−2), . . . X₀ , or sibling-to-siblingmeasures for a single snapshot, for instance, X_(n), Y_(n), and Z_(n),or projected over time, for instance, X_(n), Y_(n), Z_(n), X_(n−1),Y_(n−1), Z_(n−1), X_(n−2), Y⁻², Z_(n−2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. Again, these comparisons might be peer-to-peer measuresprojected over time, for instance, X_(n), X_(n′), X_(n″), X_(n−1),X_(n−1′), X_(n−1″), X_(n−2), X_(n−2′), X_(n−2″) . . . X₀, X_(0′),X_(0″), individual patient's measures to an average from the group.Similarly, these comparisons might be sibling-to-sibling measures forsingle snapshots, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′),Y_(n″), and Z_(n), Z_(n′), Z_(n″), or projected over time, for instance,X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n), Z_(n′), Z_(n″),X_(n−1), X_(n−1′), X_(n−1″), Y_(n−1), Y_(n−1′), Y_(n−1″), Z_(n−1),Z_(n−1′), Y_(n−1), Y_(n−1′), Y_(n−1″), Z_(n−1), Z_(n−1′), Z_(n−1″),X_(n−2), X_(n−2′), X_(n−2″), Y_(n−2), Y_(n−2′)Y_(n−2″), Z_(n−2),Z_(n−2′), Z_(n−2″) . . . X₀, X_(0′), X_(0″), Y₀, Y_(0′), Y_(0″), and Z₀,Z_(0′), Z_(0″). Other forms of comparisons are feasible.

FIG. 7 is a flow diagram showing a method 90 for automated collectionand analysis of patient information retrieved from an implantablemedical device 12 for remote patient care in accordance with the presentinvention. The method 90 is implemented as a conventional computerprogram for execution by the server system 16 (shown in FIG. 1). As apreparatory step, the patient care records are organized in the database17 with a unique patient care record assigned to each individual patient(block 91). Next, the collected measures sets for an individual patientare retrieved from the implantable medical device 12 (block 92) using aprogrammer, interrogator, telemetered signals transceiver, and the like.The retrieved collected measures sets are sent, on a substantiallyregular basis, over the internetwork 15 or similar communications link(block 93) and periodically received by the server system 16 (block 94).The collected measures sets are stored into the patient care record inthe database 17 for that individual patient (block 95). One or more ofthe collected measures sets for that patient are analyzed (block 96), asfurther described below with reference to FIG. 8. Finally, feedbackbased on the analysis is sent to that patient over the internetwork 15as an email message, via telephone line as an automated voice mail orfacsimile message, or by similar feedback communications link (block97), as further described below with reference to FIG. 11.

FIG. 8 is a flow diagram showing the routine for analyzing collectedmeasures sets 96 for use in the method of FIG. 7. The purpose of thisroutine is to make a determination of general patient wellness based oncomparisons and heuristic trends analyses of the measures, bothcollected and derived, in the patient care records in the database 17. Afirst collected measures set is selected from a patient care record inthe database 17 (block 100). If the measures comparison is to be made toother measures originating from the patient care record for the sameindividual patient (block 101), a second collected measures set isselected from that patient care record (block 102). Otherwise, a groupmeasures comparison is being made (block 101) and a second collectedmeasures set is selected from another patient care record in thedatabase 17 (block 103). Note the second collected measures set couldalso contain averaged measures for a group of disease specific patientsor for the patient population in general.

Next, if a sibling measures comparison is to be made (block 104), aroutine for comparing sibling collected measures sets is performed(block 105), as further described below with reference to FIG. 9.Similarly, if a peer measures comparison is to be made (block 106), aroutine for comparing sibling collected measures sets is performed(block 107), as further described below with reference to FIGS. 10A and10B.

Finally, a patient status indicator is generated (block 108). By way ofexample, cardiac output could ordinarily be approximately 5.0 liters perminute with a standard deviation of ±1.0. An actionable medicalphenomenon could occur when the cardiac output of a patient is ±3.0-4.0standard deviations out of the norm. A comparison of the cardiac outputmeasures 75 (shown in FIG. 5) for an individual patient against previouscardiac output measures 75 would establish the presence of any type ofdownward health trend as to the particular patient. A comparison of thecardiac output measures 75 of the particular patient to the cardiacoutput measures 75 of a group of patients would establish whether thepatient is trending out of the norm. From this type of analysis, theanalysis module 53 generates a patient status indicator 54 and othermetrics of patient wellness, as is known in the art.

FIG. 9 is a flow diagram showing the routine for comparing siblingcollected measures sets 105 for use in the routine of FIG. 8. Siblingmeasures originate from the patient care records for an individualpatient. The purpose of this routine is either to compare siblingderived measures to sibling derived measures (blocks 111-113) or siblingcollected measures to sibling collected measures (blocks 135-117). Thus,if derived measures are being compared (block 110), measures areselected from each collected measures set (block 111). First and secondderived measures are derived from the selected measures (block 112)using the derivation module 63 (shown in FIG. 4). The first and secondderived measures are then compared (block 113) using the comparisonmodule 62 (also shown in FIG. 4). The steps of selecting, determining,and comparing (blocks 111-113) are repeated until no further comparisonsare required (block 114), whereupon the routine returns.

If collected measures are being compared (block 110), measures areselected from each collected measures set (block 115). The first andsecond collected measures are then compared (block 116) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting andcomparing (blocks 115-116) are repeated until no further comparisons arerequired (block 117), whereupon the routine returns.

FIGS. 10A and 10B are a flow diagram showing the routine for comparingpeer collected measures sets 107 for use in the routine of FIG. 8. Peermeasures originate from patient care records for different patients,including groups of disease specific patients or the patient populationin general. The purpose of this routine is to compare peer derivedmeasures to peer derived measures (blocks 122-125), peer derivedmeasures to peer collected measures (blocks 126-129), peer collectedmeasures to peer derived measures (block 131-134), or peer collectedmeasures to peer collected measures (blocks 135-137). Thus, if the firstmeasure being compared is a derived measure (block 120) and the secondmeasure being compared is also a derived measure (block 121), measuresare selected from each collected measures set (block 122). First andsecond derived measures are derived from the selected measures (block123) using the derivation module 63 (shown in FIG. 4). The first andsecond derived measures are then compared (block 124) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting,determining, and comparing (blocks 122-124) are repeated until nofurther comparisons are required (block 115), whereupon the routinereturns.

If the first measure being compared is a derived measure (block 120) butthe second measure being compared is a collected measure (block 121), afirst measure is selected from the first collected measures set (block126). A first derived measure is derived from the first selected measure(block 127) using the derivation module 63 (shown in FIG. 4). The firstderived and second collected measures are then compared (block 128)using the comparison module 62 (also shown in FIG. 4). The steps ofselecting, determining, and comparing (blocks 126-128) are repeateduntil no further comparisons are required (block 129), whereupon theroutine returns.

If the first measure being compared is a collected measure (block 120)but the second measure being compared is a derived measure (block 130),a second measure is selected from the second collected measures set(block 131). A second derived measure is derived from the secondselected measure (block 132) using the derivation module 63 (shown inFIG. 4). The first collected and second derived measures are thencompared (block 133) using the comparison module 62 (also shown in FIG.4). The steps of selecting, determining, and comparing (blocks 131-133)are repeated until no further comparisons are required (block 134),whereupon the routine returns.

If the first measure being compared is a collected measure (block 120)and the second measure being compared is also a collected measure (block130), measures are selected from each collected measures set (block135). The first and second collected measures are then compared (block136) using the comparison module 62 (also shown in FIG. 4). The steps ofselecting and comparing (blocks 135-136) are repeated until no furthercomparisons are required (block 137), whereupon the routine returns.

FIG. 11 is a flow diagram showing the routine for providing feedback 97for use in the method of FIG. 7. The purpose of this routine is toprovide tiered feedback based on the patient status indicator. Fourlevels of feedback are provided with increasing levels of patientinvolvement and medical care intervention. At a first level (block 150),an interpretation of the patient status indicator 54, preferably phrasedin lay terminology, and related health care information is sent to theindividual patient (block 151) using the feedback module 55 (shown inFIG. 3). At a second level (block 152), a notification of potentialmedical concern, based on the analysis and heuristic trends analysis, issent to the individual patient (block 153) using the feedback module 55.At a third level (block 154), the notification of potential medicalconcern is forwarded to the physician responsible for the individualpatient or similar health care professionals (block 155) using thefeedback module 55. Finally, at a fourth level (block 156),reprogramming instructions are sent to the implantable medical device 12(block 157) using the feedback module 55.

FIG. 12 is a block diagram showing a system 200 for providing normalizedvoice feedback from an individual patient 11 in an automated collectionand analysis patient care system, such as the system 10 of FIG. 1. Theremote client 18 includes a microphone 201 and a speaker 202 which isinterfaced internally within the remote client 18 to sound recordationand reproduction hardware. The patient 11 provides spoken feedback intothe microphone 201 in response to voice prompts reproduced by the remoteclient 18 on the speaker 202, as further described below with referenceto FIG. 13. The raw spoken feedback is processed into a normalized setof quality of life measures which each relate to uniform self-assessmentindicators, as further described below with reference to FIG. 15.Alternatively, in a further embodiment of the system 200, the patient 11can provide spoken feedback via a telephone network 203 using a standardtelephone 203, including a conventional wired telephone or a wirelesstelephone, such as a cellular telephone, as further described below withreference to FIG. 20. In the described embodiment, the microphone 201and the speaker 202 are standard, off-the-shelf components commonlyincluded with consumer personal computer systems, as is known in theart.

The system 200 continuously monitors and collects sets of devicemeasures from the implantable medical device 12. To augment the on-goingmonitoring process with a patient's self-assessment of physical andemotional well-being, a quality of life measures set can be recorded bythe remote client 18 Importantly, each quality of life measures set isrecorded substantially contemporaneous to the collection of anidentified collected device measures set. The date and time of day atwhich the quality of life measures set was recorded can be used tocorrelate the quality of life measures set to the collected devicemeasures set recorded closest in time to the quality of life measuresset. The pairing of the quality of life measures set and an identifiedcollected device measures set provides medical practitioners with a morecomplete picture of the patient's medical status by combiningphysiological “hard” machine-recorded data with semi-quantitative “soft”patient-provided data.

FIG. 13 is a block diagram showing the software modules of the remoteclient 18 of the system 200 of FIG. 12. As with the software modules ofthe system 10 of FIG. 1, each module here is also a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU as object or byte code, as is known in the arts. There are twobasic software modules, which functionally define the primary operationsperformed by the remote client 18 in providing normalized voicefeedback: audio prompter 210 and speech engine 214. The remote client 18includes a secondary storage 219, such as a hard drive, a CD ROM player,and the like, within which is stored data used by the software modules.Conceptually, the voice reproduction and recognition functions performedby the audio prompter 210 and speech engine 214 can be describedseparately, but those same functions could also be performed by a singlevoice processing module, as is known in the art.

The audio prompter 210 generates voice prompts 226 which are played backto the patient 11 on the speaker 202. Each voice prompt is in the formof a question or phrase seeking to develop a self-assessment of thepatient's physical and emotional well being. For example, the patient 11might be prompted with, “Are you short of breath?” The voice prompts 226are either from a written script 220 reproduced by speech synthesizer211 or pre-recorded speech 221 played back by playback module 212. Thewritten script 220 is stored within the secondary storage 219 andconsists of written quality of life measure requests. Similarly, thepre-recorded speech 221 is also stored within the secondary storage 219and consists of sound “bites” of recorded quality of life measurerequests in either analog or digital format.

The speech engine 214 receives voice responses 227 spoken by the patient11 into the microphone 201. The voice responses 227 can be unstructured,natural language phrases and sentences. A voice grammar 222 provides alexical structuring for use in determining the meaning of each spokenvoice response 227. The voice grammar 222 allows the speech engine 214to “normalize” the voice responses 227 into recognized quality of lifemeasures 228. Individual spoken words in each voice response 227 arerecognized by a speech recognition module 215 and translated intowritten words. In turn, the written words are parsed into tokens by aparser 216. A lexical analyzer 217 analyzes the tokens as completephrases in accordance with a voice grammar 222 stored within thesecondary storage 219. Finally, if necessary, the individual words arenormalized to uniform terms by a lookup module 218 which retrievessynonyms maintained as a vocabulary 223 stored within the secondarystorage 218. For example, in response to the query. “Are you short ofbreath?,” a patient might reply, “I can hardly breath,” “I am panting,”or “I am breathless.” The speech recognition module 215 would interpretthese phrases to imply dyspnea with a corresponding quality of lifemeasure indicating an awareness by the patient of abnormal breathing. Inthe described embodiment, the voice reproduction and recognitionfunctions can be performed by the various natural voice softwareprograms licensed by Dragon Systems, Inc., Newton, Mass. Alternatively,the written script 220, voice grammar 222, and vocabulary 223 could beexpressed as a script written in a voice page markup language forinterpretation by a voice browser operating on the remote client 18. Twoexemplary voice page description languages include the VoxML markuplanguage, licensed by Motorola, Inc., Chicago, Ill., and described athttp://www.voxml.com, and the Voice eXtensible Markup Language (VXML),currently being jointly developed by AT&T, Motorola, LucentTechnologies, and IBM, and described at http:/www.vxm.lforum.com. Themodule functions are further described below in more detail beginningwith reference to FIGS. 16A-16B.

FIG. 14 is a block diagram showing the software modules of the serversystem 16 of the system 200 of FIG. 12. The database module 51,previously described above with reference to FIG. 3, also receives thecollected quality of life measures set 228 from the remote client 18,which the database module 51 stores into the appropriate patient carerecord in the database 52. The date and time of day 236 (shown in FIG.15) of the quality of life measures set 228 is matched to the date andtime of day 73 (shown in FIG. 5) of the collected measures set 50recorded closest in time to the quality of life measures set 228. Thematching collected measures set 50 is identified in the patient carerecord and can be analyzed with the quality of life measures set 228 bythe analysis module 53, such as described above with reference to FIG.8.

FIG. 15 is a database schema showing, by way of example, theorganization of a quality of life record 230 for cardiac patient carestored as part, of a patient care record in the database 17 of thesystem 200 of FIG. 12. A quality of life score is a semi-quantitativeself-assessment of an individual patient's physical and emotional wellbeing. Non-commercial, non-proprietary standardized automated quality oflife scoring systems are readily available, such as provided by the DukeActivities Status Indicator. For example, for a cardiac patient, thequality of life record 230 stores the following information; healthwellness 231, shortness of breath 232, energy level 233, chestdiscomfort 235, time of day 234, and other quality of life measures aswould be known to one skilled in the art. Other types of quality of lifemeasures are possible.

A quality of life indicator is a vehicle through which a patient canremotely communicate to the patient care system how he or she issubjectively feeling. The quality of life indicators can includesymptoms of disease. When tied to machine-recorded physiologicalmeasures, a quality of life indicator can provide valuable additionalinformation to medical practitioners and the automated collection andanalysis patient care system 200 not otherwise discernible withouthaving the patient physically present. For instance, a scoring systemusing a scale of 1.0 to 10.0 could be used with 10.0 indicating normalwellness and 1.0 indicating severe health problems. Upon the completionof an initial observation period, a patient might indicate a healthwellness score 231 of 5.0 and a cardiac output score of 5.0. After onemonth of remote patient care, the patient might then indicate a healthwellness score 231 of 4.0 and a cardiac output score of 4.0 and a weeklater indicate a health wellness score 231 of 3.5 and a cardiac outputscore of 3.5. Based on a comparison of the health wellness scores 231and the cardiac output scores, the system 200 would identify a trendindicating the necessity of potential medical intervention while acomparison of the cardiac output scores alone might not lead to the sameprognosis.

FIGS. 16A-16B are flow diagrams showing a method 239 for providingnormalized voice feedback from an individual patient 11 in an automatedcollection and analysis patient care system 200. As with the method 90of FIG. 7, this method is also implemented as a conventional computerprogram and performs the same set of steps as described with referenceto FIG. 7 with the following additional functionality. First, voicefeedback spoken by the patient 11 into the remote client 18 isprocessed, into a quality of life measures set 228 (block 240), asfurther described below with reference to FIG. 17. The voice feedback isspoken substantially contemporaneous to the collection of an identifieddevice measures set 50. The appropriate collected device measures set 50can be matched to and identified with (not shown) the quality of lifemeasures set 228 either by matching their respective dates and times ofday or by similar means, either by the remote client 18 or the serversystem 16. The quality of life measures set 228 and the identifiedcollected measures set 50 are sent over the internetwork 15 to theserver system 16 (block 241). Note the quality of life measures set 228and the identified collected measures set 50 both need not be sent overthe internetwork 15 at the same time, so long as the two sets areultimately paired based on, for example, date and time of day. Thequality of life measures set 228 and the identified collected measuresset 50 are received by the server system 16 (block 242) and stored inthe appropriate patient care record in the database 52 (block 243).Finally, the quality of life measures set 228, identified collectedmeasures set 50, and one or more collected measures sets 50 are analyzed(block 244) and feedback, including a patient status indicator 54 (shownin FIG. 14), is provided to the patient (block 245).

FIG. 17 is a flow diagram showing the routine for processing voicefeedback 240 for use in the method of FIGS. 16A-16B. The purpose of thisroutine is to facilitate a voice interactive session with the patient 11during which is developed a normalized set of quality of life measures.Thus, the remote client 18 requests a quality of life measure via avoice prompt (block 250), played on the speaker 202 (shown in FIG. 13),as further described below with reference to FIG. 18. The remote client18 receives the spoken feedback from the patient 11 (block 251) via themicrophone 201 (shown in FIG. 13). The remote client 18 recognizesindividual words in the spoken feedback and translates those words intowritten words (block 252), as farther described below with reference toFIG. 19. The routine returns at the end of the voice interactivesession.

FIG. 18 is a flow diagram showing the routine for requesting a qualityof life measure 251 for use in the routine 240 of FIG. 17. The purposeof this routine is to present a voice prompt 226 to the user via thespeaker 202. Either pre-recorded speech 221 or speech synthesized from awritten script 220 can be used. Thus, if synthesized speech is employedby the remote client 18 (block 260), a written script, such as a voicemarkup language script, specifying questions and phrases which with torequest quality of life measures is stored (block 261) on the secondarystorage 219 of the remote client 18. Each written quality of lifemeasure request is retrieved by the remote client 18 (block 262) andsynthesized into speech for playback to the patient 11 (block 263).Alternatively, if pre-recorded speech is employed by the remote client18 (block 260), pre-recorded voice “bites” are stored (block 264) on thesecondary storage 219 of the remote client 18. Each pre-recorded qualityof life measure request is retrieved by the remote client 18 (block 265)and played back to the patient 11 (block 266). The routine then returns.

FIG. 19 is a flow diagram showing the routine for recognizing andtranslating individual spoken words 252 for use in the routine 240 ofFIG. 17. The purpose of this routine is to receive and interpret afree-form voice response 227 from the user via the microphone 201.First, a voice grammar consisting of a lexical structuring of words,phrases, and sentences is stored (block 270) on the secondary storage219 of the remote client 18. Similarly, a vocabulary of individual wordsand their commonly accepted synonyms is stored (block 271) on thesecondary storage 219 of the remote client 18. After individual words inthe voice feedback are recognized (block 272), the individual words areparsed into tokens (block 273). The voice feedback is then lexicallyanalyzed using the tokens and in accordance with the voice grammar 222(block 274) to determine the meaning of the voice feedback. Ifnecessary, the vocabulary 223 is referenced to lookup synonyms of theindividual words (block 275). The routine then returns.

FIG. 20 is a block diagram showing the software modules of the serversystem in a further embodiment of the system 200 of FIG. 12. Thefunctionality of the remote client 18 in providing normalized voicefeedback is incorporated directly into the server system 16. The system200 of FIG. 12 requires the patient 11 to provide spoken feedback via alocally situated remote client 18. However, the system 280 enables apatient 11 to alternatively provide spoken feedback via a telephonenetwork 203 using a standard telephone 203, including a conventionalwired telephone or a wireless telephone, such as a cellular telephone.The server system 16 is augmented to include the audio prompter 210, thespeech engine 214, and the data stored in the secondary storage 219. Atelephonic interface 280 interfaces the server system 16 to thetelephone network 203 and receives voice responses 227 and sends voiceprompts 226 to and from the server system 16. Telephonic interfacingdevices are commonly known in the art.

FIG. 21 is a block diagram showing a system for providing normalizedvoice feedback from an individual patient in an automated collection andanalysis patient care system 300 in accordance with a further embodimentof the present invention. The system 300 provides remote patient care ina manner similar to the system 200 of FIG. 12, but with additionalfunctionality for diagnosing and monitoring multiple sites within apatient's body using a variety of patient sensors for diagnosing one ormore disorder. The patient 301 can be the recipient of an implantablemedical device 302, as described above, or have an external medicaldevice 303 attached, such as a Holter monitor-like device for monitoringelectrocardiograms. In addition, one or more sites in or around thepatient's body can be monitored using multiple sensors 304 a, 304 b,such as described in U.S. Pat. Nos. 4,987,897; 5,040,536; 5,113,859; and5,987,352, the disclosures of which are incorporated herein byreference. One automated system and method for collecting and analyzingretrieved patient information suitable for use with the presentinvention is described in the related, commonly-owned U.S. Pat. No.6,270,457, issued Aug. 7, 2001, the disclosure of which is incorporatedherein by reference. Other types of devices with physiological measuresensors, both heterogeneous and homogenous, could be used, either withinthe same device or working in conjunction with each other, as is knownin the art.

As part of the system 300, the database 17 stores patient care records305 for each individual patient to whom remote patient care is beingprovided. Each patient care record 305 contains normal patientidentification and treatment profile information, as well as medicalhistory, medications taken, height and weight, and other pertinent data(not shown). The patient care records 305 consist primarily ofmonitoring sets 306 storing device and derived measures (D&DM) sets 307and quality of life and symptom measures (QOLM) sets 308 recorded anddetermined thereafter on a regular, continuous basis. The organizationof the device and derived measures sets 305 for an exemplary cardiacpatient care record is described above with reference to FIG. 5. Theorganization of the quality of life and symptom measures sets 308 isfurther described below with reference to FIG. 23.

Optionally, the patient care records 305 can further include a referencebaseline 309 storing a special set of device and derived referencemeasures sets 310 and quality of life and symptom measures sets 311recorded and determined during an initial observation period, such asdescribed in the related, commonly-owned U.S. Pat. No. 6,280,380, issuedAug. 28, 2001, the disclosure of which is incorporated herein byreference. Other forms of database organization are feasible.

Finally, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider312 using feedback means similar to that used to notify the patient. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. Furthermore, the spoken voicefeedback from the patient and the feedback provided by the system 200can be communicated by means of or in combination with the medicaldevice itself, whether implantable, external or otherwise.

FIG. 22 is a block diagram showing the analysis module 53 of die serversystem 16 of FIG. 21. The peer collected measures sets 60 and siblingcollected measures sets 61 can be organized into site specific groupingsbased on the sensor from which they originate, that is, implantablemedical device 302, external medical device 303, or multiple sensors 304a, 304 b. The functionality of the analysis module 53 is augmented toiterate through a plurality of site specific measures sets 315 and oneor more disorders.

As described above, as an adjunct to remote patient care through themonitoring of measured physiological data via implantable medical device302, external medical device 303 and multiple sensors 304 a, 304 b,quality of life and symptom measures sets 308 can also be stored in thedatabase 17 as part of the monitoring sets 306. A quality of lifemeasure is a semi-quantitative self-assessment of an individualpatient's physical and emotional well-being and a record of symptoms,such as provided by the Duke Activities Status Indicator. These scoringsystems can be provided for use by the patient 11 on the personalcomputer 18 (shown in FIG. 1) to record his or her quality of lifescores for both initial and periodic download to the server system 16.

FIG. 23 is a database schema which augments the database schemadescribed above with reference to FIG. 15 and showing, by way ofexample, the organization of a quality of life and symptom measures setrecord 320 for care of patients stored as part of a patient care record305 in the database 17 of the system 300 of FIG. 21. The followingexemplary information is recorded for a patient: overall health wellness321, psychological state 322, chest discomfort 323, location of chestdiscomfort 324, palpitations 325, shortness of breath 326, exercisetolerance 327, cough 328, sputum production 329, sputum color 330,energy level 331, syncope 332, near syncope 333, nausea 334, diaphoresis335, time of day 91, and other quality of life and symptom measures aswould be known to one skilled, in the art.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W. B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 307 and the symptoms during the six-minute walk toquality of life and symptom measures sets 308.

FIG. 24 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database 17 of the system 300of FIG. 21. Three patient care records are again shown for Patient 1,Patient 2, and Patient 3 with each of these records containing sitespecific measures sets 315, grouped as follows. First, the patient carerecord for Patient 1 includes three site specific measures sets A, B andC, corresponding to three sites on Patient 1's body. Similarly, thepatient care record for Patient 2 includes two site specific measuressets A and B, corresponding to two sites, both of which are in the samerelative positions on Patient 2's body as the sites for Patient 1.Finally, the patient care record for Patient 3 includes two sitespecific measures sets A and D, also corresponding to two medical devicesensors, only one of which, Site A, is in the same relative position asSite A for Patient 1 and Patient 2.

The analysis module 53 (shown in FIG. 22) performs two further forms ofcomparison in addition to comparing the individual measures for a givenpatient to other individual measures for that same patient or to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. First, the individual measures corresponding to each body sitefor an individual patient can be compared to other individual measuresfor that same patient, a peer group or a general patient population.Again, these comparisons might be peer-to-peer measures projected overtime, for instance, comparing measures for each site. A, B and C forPatient 1, X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) , X_(n−1) _(A) ,X_(n−1′) _(A) , X_(n−1′) _(A) , X_(n−2) _(A) , X_(n−2′) _(A) , X_(n−2″)_(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″) _(A) ; X_(n) _(B) , X_(n′)_(B) , X_(n″) _(B) , X_(n−1) _(B) , X_(n−1′) _(B) , X_(n−1″) _(B) ,X_(n−2) _(B) , X_(n−2′) _(B) , X_(n−2″) _(B) . . . X₀ _(S) , X_(0′) _(S), X_(0″) _(S) , X_(nc), X_(n′) _(C) , X_(n″) _(C) , X_(n−1) _(C) ,X_(n−1′) _(C) , X_(n−1″) _(C) , X_(n−2) _(C) , X_(n−2′) _(C) , X_(n−2′)_(C) . . . X₀ _(C) , X_(0′) _(C) , X_(0″) _(C) ; comparing comparablemeasures for Site A for the three patients, X_(n) _(A) , X_(n′) _(A) ,X_(n″) _(A) , X_(n−1) _(A) , X_(n−1′) _(A) , X_(n−″) _(A) , X_(n−2) _(A), X_(n−2′) _(A) , X_(n−2″) _(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″)_(A) ; or comparing the individual patient's measures to an average fromthe group. Similarly, these comparisons might be sibling-to-siblingmeasures for single snapshots, for instance, comparing comparablemeasures for Site A for the three patients, X_(n) _(A) , X_(n′) _(A) ,X_(n″) _(A) , Y_(n) _(A) , Y_(n′) _(A) , Y_(n″) _(S) , and Z_(n) _(A) ,Z_(n′) _(A) , Z_(n″) _(A) , or comparing those same comparable measuresfor Site A projected over time, for instance, X_(n) _(A) , X_(n′) _(A) ,X_(n″) _(A) , Y_(n) _(A) , Y_(n′) _(A) , Y_(n″) _(A) , Z_(n) _(A) ,Z_(n′) _(A) , Z_(n″) _(A) , X_(n−1) _(A) , X_(n−1′) _(A) , X_(n−1″) _(A), Y_(n−1) _(A) , Y_(n−1′) _(A) , Y_(n−1″) _(A) , Z_(n−1) _(A) , Z_(n−1′)_(A) , Z_(n−1″) _(A) , X_(n−2) _(A) , X_(n−2′) _(A) , X_(n−2″) _(A)Y_(n−2) _(A) , Y_(n−2′) _(A) , Y_(n−2″) _(A) Z_(n−2) _(A) , Z_(n−2′)_(A) , Z_(n−2″) _(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″) _(A) , Y₀ _(A), Y_(0′) _(A) , Y_(0″) _(a) , and Z₀ _(A) , Z_(0′) _(A) , Z_(0″) _(A) .Other forms of site-specific comparisons, including comparisons betweenindividual measures from non-comparable sites between patients, arefeasible.

Second, the individual measures can be compared on a disorder specificbasis. The individual measures stored in each cardiac patient record canbe logically grouped into measures relating to specific disorders anddiseases, for instance, congestive heart failure, myocardial infarction,respiratory distress, and atrial fibrillation. The foregoing comparisonoperations performed by the analysis module 53 are further describedbelow with reference to FIGS. 26A-26B.

FIG. 25 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 305 of FIG. 24. Each patientcare record 305 includes characteristics data 350, 351, 352, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of ailcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

FIGS. 26A-26B are flow diagrams showing a method for providingnormalized voice feedback from an individual patient in an automatedcollection and analysis patient care system 360 in accordance with afurther embodiment of the present invention. As with the method 239 ofFIGS. 16A and 16B, this method is also implemented as a conventionalcomputer program and performs the same set of steps as described withreference to FIGS. 16A and 16B with the following additionalfunctionality. As before, the patient care records are organized in thedatabase 17 with a unique patient care record assigned to eachindividual patient (block 361). Next, the individual measures for eachsite are iteratively obtained in a first processing loop (blocks362-367) and each disorder is iteratively analyzed in a secondprocessing loop (blocks 368-370). Other forms of flow control arefeasible, including recursive processing.

During each iteration of the first processing loop (blocks 362-367), thecollected measures sets for an individual patient are retrieved from themedical device or sensor located at the current site (block 363) using aprogrammer, interrogator, telemetered signals transceiver, and the like.The retrieved collected measures sets are sent, on a substantiallyregular basis, over the internetwork 15 or similar communications link(block 364) and periodically received by the server system 16 (block365). The collected measures sets are stored into the patient carerecord 305 in the database 17 for that individual patient (block 366).Any voice feedback spoken by the patient 11 into the remote client 18 isprocessed into a quality of life measures set 228 (block 240), asdescribed above with reference to FIG. 17. The voice feedback is spokensubstantially contemporaneous to the collection of an identified devicemeasures set 50. The appropriate collected device measures set 50 can bematched to and identified with (not shown) the quality of life measuresset 228 either by matching their respective dates and times of day or bysimilar means, either by the remote client 18 or the server system 16.The qualify of life measures set 228 and the identified collectedmeasures set 50 are sent over the internetwork 15 to the server system16 (block 241). The quality of life measures set 228 and the identifiedcollected measures set 50 are received by the server system 16 (block242) and stored in the appropriate patient care record in the database52 (block 243).

During each iteration of the second processing loop (blocks 368-370),the quality of life measures set 228, identified collected measures set50, and one or more of the collected measures sets for that patient areanalyzed for the current disorder are analyzed (block 244). Finally,feedback based on the analysis is sent to that patient over theinternetwork 15 as an email message, via telephone line as an automatedvoice mail or facsimile message, or by similar feedback communicationslink (block 245). In addition, the measures sets can be furtherevaluated and matched to diagnose specific medical disorders, such ascongestive heart failure, myocardial infarction, respiratory distress,and atrial fibrillation, as described in related, commonly-owned U.S.Pat. No. 6,336,903, issued Jan. 8, 2002; U.S. Pat. No. 6,368,284, issuedApr. 9, 2002; U.S. Pat. No. 6,398,728, issued Jun. 4, 2002; and U.S.Pat. No. 6,411,840, issued Jun. 25, 2002, the disclosures of which areincorporated herein by reference. In addition, multiplenear-simultaneous disorders can be ordered and prioritized as part ofthe patient status indicator as described in the related, commonly-ownedU.S. Pat. No. 6,440,066, issued Aug. 27, 2002, the disclosure of whichis incorporated herein by reference.

Therefore, through the use of the collected measures sets, the presentinvention makes possible immediate access to expert medical care at anytime and in any place. For example, after establishing and registeringfor each patient an appropriate baseline set of measures, the databaseserver could contain a virtually up-to-date patient history, which isavailable to medical providers for the remote diagnosis and preventionof serious illness regardless of the relative location of the patient ortime of day.

Moreover, the gathering and storage of multiple sets of critical patientinformation obtained on a routine basis makes possible treatmentmethodologies based on an algorithmic analysis of the collected datasets. Each successive introduction of a new collected measures set intothe database server would help to continually improve the accuracy andeffectiveness of the algorithms used. In addition, the present inventionpotentially enables the detection, prevention, and cure of previouslyunknown forms of disorders based on a trends analysis and by across-referencing approach to create continuously improving peer-groupreference databases.

Similarly, the present invention makes possible the provision of tieredpatient feedback based on the automated analysis of the collectedmeasures sets. This type of feedback system is suitable for use in, forexample, a subscription based health care service. At a basic level,informational feedback can be provided by way of a simple interpretationof the collected data. The feedback could be built up to provide agradated response to the patient, for example, to notify the patientthat he or she is trending into a potential trouble zone. Humaninteraction could be introduced, both by remotely situated and localmedical practitioners. Finally, the feedback could include directinterventive measures, such as remotely reprogramming a patient's IPG.

Finally, the present invention allows “live” patient voice feedback tobe captured simultaneously with the collection of physiological measuresby their implantable medical device. The voice feedback is normalized toa standardized set of quality of life measures which can be analyzed ina remote, automated fashion. The voice feedback could also be coupledwith visual feedback, such as through digital photography or video, toprovide a more complete picture of the patient's physical well-being.

FIG. 27 is a block diagram showing a system for providing directnormalized voice feedback from an individual patient in art automatedcollection and analysis patient care system 400 in accordance with afurther embodiment of the present invention. The system 400 providesremote patient care in a manner similar to the system 200 of FIG. 21.However, the system 400 provides additional functionality for receivingvoice feedback spoken directly by and for sending voice prompts to thepatient 301. The patient 301 is either a recipient of an implantablemedical device 302 or is monitored by an external medical device 303.Additionally, a microphone 401 and a speaker 402 are implanted into thepatient 301, either separately (as shown) or as part of either theimplantable medical device 302, such as described in U.S. Pat. No.5,888,187, issued Mar. 30, 1999 to Jaeger et al, the disclosure of whichis incorporated by reference. The microphone 401 and speaker 402, ifseparate from the implantable medical device 302, are implanted into thesubcutaneous or other tissues of the chest or abdomen of the patient301. The microphone 401 and speaker 402 form a patient communicationsinterface. In the described embodiment, the microphone 401 and speaker402 are implanted subcutaneously above the pectoralis major muscle, butother implantation sites could be used, as indicated by the condition ofthe patient 301.

The microphone 401 includes an electronics package to receive, process,store and transmit spoken voice signals. The speaker 402 includes anelectronics package to receive, process, store and playback spoken voiceprompts. The microphone 401 and speaker 402 interface through atransmitter coil to non-invasively communicate with the programmer 14over an inductive connection or via a wireless radio frequency link to aradio frequency (RF) receiver 404. The programmer 14 is remotelyinterfaced to the system server 16 via the internetwork 15, whichgenerates outgoing voice prompts and processes incoming voice feedback.The electronics packages and transmitter coils are hermetically-sealedwithin housings constructed of biocompatible materials, if separate fromthe similarly hermetically-sealed implantable medical device 302. In thedescribed embodiment, the microphone 401 and speaker 402 are stand aloneunits that operate as adjuncts to the implantable medical device 302. Ina further embodiment, the microphone 401 and speaker 402 areincorporated directly into the implantable medical device 302. Otherarrangements and layouts of patient communications components arepossible.

Following implantation, the patient communications interface can beaccessed either upon the initiative of the patient or by the serversystem 16. Generally, the electronics package in the speaker 402 isactivated by voice prompts received from the server system 16 via theprogrammer 14 and the voice prompts are played back to the patient 301.The electronics package in the microphone 401 is activated by the spokenspeech of the patient 301, such as through a special spoken keywordpre-assigned to the patient 301 or alternatively is activated by a tapcode- or motion-activated switch that the patient 301 could respectivelytap or press for attention. Other types of microphone activationmechanisms are possible. Once activated, the patient 11 provides spokenfeedback directly into the microphone 401, either spontaneously or inresponse to the voice prompts reproduced by speaker 402. In a furtherembodiment, the voice prompts are played back by the speaker 202 andvoice feedback is received by the microphone 201, both provided by theremote client 18, as further described above with reference to FIG. 13.Other voice prompting and voice feedback configurations are possible.

Patient initiated communications sessions begin with the patientactivating the patient communications interface through a physicalmaneuver, such as a voice code, tap code or combination voice and tapcode, including deliberate pauses between each word. The patientcommunications interlace authenticates the patient based on pre-defineduniquely identifying patient characteristics, such as a speechfingerprint, teeth-click code, phonic tone code, cough code or tap code.Other types of activation codes and patient identifying characteristicsare possible. Following authentication, the patient communicationssession is conducted by the patient providing spoken information and thesystem providing verbal system information respectively via themicrophone 401 and speaker 402. In a further embodiment, the spokenpatient information is analyzed for symptomatic expression and emotionalstate relating to the monitored health conditions. Finally, the sessionterminates upon the initiative of either the patient or the serversystem 16.

In a further embodiment, different activation codes can triggerdifferent responses from the system. For example, a pair of tapsfollowed by the spoken request, “talk to me,” can result in the serversystem 16 responding with, “what information are you seeking?”Similarly, a pair of taps followed by the spoken request, “help me,” canresult in the server system 16 generating responding with, “shall I call911?” Finally, a pair of taps followed by the spoken request, “I don'tfeel” can result in a series of interrogative questions from the serversystem 16, including, “Are you short of breath,” “Do you have chestpain,” and “Do you feel faint?” Other types of levels are formats ofactivation codes and responses are possible.

System initiated communications sessions can be triggered based onpre-defined conditions or as a result of continuous monitoring performedby the server system 16. The server system 16 can automatically detectpre-defined conditions under which a patient communications session canbe established, such as the time of day or a sequence of specificevents, such as a defibrillation, accumulating lung water, excessivenighttime coughing, recurrent disrupted sleep patterns, sudden changesin respiration patterns at rest or amount of exertion, decreasing levelsof heart rate variability, or physical activity. The server system 16can also initiate a patient communications session based on thecontinuous monitoring of the physiological measures, such as when themeasures reach a certain level or rate of change. Consequently, thepatient communications session is initiated either upon the detection ofthe triggering conditions, if applicable, or at the next appropriatetime. The patient is discreetly alerted, such as through audio or tapcodes to indicate a willingness to open a patient communicationssession, after which the session is conducted in the usual fashion.

The electronics package processes the spoken speech into a processedsignal 403, either in analog or digital form, that is stored in a memoryprovided with, the microphone 401. On a regular basis, the processedsignal 403 is retrieved from the memory and transmitted, to the receiver404, generally along with the telemetered signals stored in theimplantable medical device 12. Alternatively, the processed voice signalcan be retrieved from the memory immediately or transmitted to thereceiver 404 immediately, depending upon need and urgency. The retrievedprocessed signal 403 is then processed into a normalized set of qualityof life measures 228, as further described above with reference to FIG.13, which each relate to uniform self-assessment indicators, as furtherdescribed above with reference to FIG. 15.

The processed signal 403 of the voice feedback is broadcast from themicrophone 401 to the receiver 404, which is incorporated into theprogrammer 14. Similarly, a data signal of the voice prompts isbroadcast from the receiver 404 to the speaker 402. The receiver 404includes an electronics package to receive, process, store, download andupload processed and data signals. The receiver 404 receives andprocesses the processed signal 403 broadcast from the microphone 401,which is then converted, if necessary, into a digitized form suitablefor transmission over the internetwork 15 and stored until thecorresponding telemetered signals are next retrieved. The receiver 404also receives and processes the data signal sent from the server system16, which is then converted, if necessary, into an analog or digitalform suitable for broadcasting to and playback by the speaker 402. Thereceiver 404 could also be incorporated into any form of programmer,interrogator, recorder, monitor, or telemetered signals transceiversuitable for communicating with an implantable medical device 12, as isknown in the art. In addition, the receiver 404 could be a stand aloneunit, or incorporated into a personal computer or digital data processorthat could be interfaced to the implantable medical device 12. Otherconfigurations and arrangements of the receiver 404 are possible.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1-19. (canceled)
 20. A system for processing directly-collectednormalized voice feedback for use in automated patient care, comprising:a database storing patient care records with each record containing oneor more physiological measures relating to individual patientinformation and one or more quality of life measures relating tonormalized spoken patient self-assessment indicators, each quality oflife measure recorded substantially contemporaneous to the physiologicalmeasures through an implantable microphone; and an analysis moduleanalyzing the physiological measures and the contemporaneously recordedquality of life measures retrieved from one such patient care record todetermine a patient status.
 21. A system according to claim 20, furthercomprising: a medical device having a sensor for monitoring andrecording the physiological measures from an anatomical site at leastone of directly and derivatively.
 22. A system according to claim 21,further comprising: at least one further sensor monitoring and recordingthe physiological measures from an anatomical site unique from theanatomical site monitored by any other such sensor; and the analysismodule determining the patient status by comparing physiologicalmeasures retrieved from one such patient care record for a plurality ofsensors.
 23. A system according to claim 20, further comprising: thedatabase storing patient care records with each record containingphysiological measures from sensors monitoring a plurality of anatomicalsites within the individual patient; and the analysis module analyzingthe physiological measures from one such patient care record relative toeach anatomical site.
 24. A system according to claim 20, the remoteclient further comprising: a feedback device interfacing and processingthe quality of life measures from the individual patient.
 25. A systemaccording to claim 20, further comprising: a audio prompter generatingvoice prompts; and an implantable speaker playing the voice prompts. 26.A method for processing directly-collected normalized voice feedback foruse in automated patient care, comprising: retrieving one or morephysiological measures relating to individual patient information andone or more quality of life measures relating to normalized spokenpatient self-assessment indicators from a patient care record, eachquality of life measure recorded substantially contemporaneous to thephysiological measures through an implantable microphone; and analyzingthe physiological measures and the contemporaneously recorded quality oflife measures retrieved from one such patient care record to determine apatient status.
 27. A method according to claim 26, further comprising:obtaining the physiological measures from a medical device having asensor for monitoring the physiological measures from an anatomical siteat least one of directly and derivatively.
 28. A method according toclaim 27, further comprising: obtaining the physiological measuresmonitored by at least one further sensor monitoring the physiologicalmeasures from an anatomical site unique from the anatomical sitemonitored by any other such sensor; and determining the patient statusby comparing physiological measures retrieved from one such patient carerecord for a plurality of sensors.
 29. A method according to claim 26,further comprising: receiving the physiological measures from sensorsmonitoring a plurality of anatomical sites within the individual patientfrom a patient care record; and analyzing the physiological measuresrelative to each anatomical site.
 30. A method according to claim 26,the remote client further comprising: interfacing and processing thequality of life measures from the individual patient.
 31. A methodaccording to claim 26, further comprising: generating voice prompts; andplaying the voice prompts on an implantable speaker.
 32. Acomputer-readable storage medium for a device holding code forperforming the method according to claim
 26. 33-49. (canceled)